gam.side.conditions           package:mgcv           R Documentation

_I_d_e_n_t_i_f_i_a_b_i_l_i_t_y _s_i_d_e _c_o_n_d_i_t_i_o_n_s _f_o_r _a _G_A_M.

_D_e_s_c_r_i_p_t_i_o_n:

     GAM formulae with repeated variables only correspond to
     identifiable models given some side conditions on the null space
     of the model penalties. This routine works out appropriate side
     conditions and returns the coefficient matrix that imposes them.
     It is called from 'gam' and is not intended to be called by users.

_U_s_a_g_e:

     gam.side.conditions(G)

_A_r_g_u_m_e_n_t_s:

       G: the object obtained by calling 'gam.setup' followed by
          'GAMsetup' 

_D_e_t_a_i_l_s:

     Part of the basis of a thin plate regression spline (of the sort
     used to define GAMs in package 'mgcv') is a polynomial basis for
     the space of functions of zero wiggliness according to the spline
     wiggliness penalty. A GAM with repeated dependence on one or more
     variables will have multiple copies of these basis terms, and will
     hence not be identifiable without extra constraints. For example,
     the design matrix for the model  'y~s(x)+s(z)+s(x,z)' will feature
     two copies of the columns for 'x' and 'z', but by imposing the
     constraint that the second parameter for each repeat variable is
     zero, the model becomes identifiable again.

     This routine automates the process of producing these constraints
     in general. The method used is to create labels for each null
     basis column that uniquely identify the term while allowing
     multiple copies of the column to be identified. The algorithm
     works upwards throught the dimensions - i.e. starts with 1-d
     terms, then does 2-d terms , then 3-d and so on. A stack of null
     basis columns is maintained, and each new basis term tested
     against it - if the new term is a repeat then its parameter is
     constrained to be zero - otherwise it is added to the stack. The
     method is symbolic and not numerical - you can fool it by making
     two copies of the same covariate with different names.

     If the model can not be made identifiable then an error is
     generated.

     The code critically depends on equivalence between the R code
     null.space.basis.powers and the equivalent C code in tprs.c.

     Note that the routine does nothing to the constant terms in the
     basis - these are already dealt with by the constraints that
     centre all smooths.

_V_a_l_u_e:

     A matrix C defining the constraints on the model. The constraints
     are that Cp=0 where p is the parameter vector. 'C' includes the
     original sum to zero constraints from 'G$C' and is hence suitable
     for directly replacing that matrix.

_A_u_t_h_o_r(_s):

     Simon N. Wood simon@stats.gla.ac.uk

_R_e_f_e_r_e_n_c_e_s:

     Wood, S.N. (2000)  Modelling and Smoothing Parameter Estimation
     with Multiple  Quadratic Penalties. J.R.Statist.Soc.B
     62(2):413-428

     Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B
     65(1):95-114

     <URL: http://www.stats.gla.ac.uk/~simon/>

_E_x_a_m_p_l_e_s:

     set.seed(0)
     n<-400
     sig2<-4
     x0 <- runif(n, 0, 1)
     x1 <- runif(n, 0, 1)
     x2 <- runif(n, 0, 1)
     pi <- asin(1) * 2
     f <- 2 * sin(pi * x0)
     f <- f + exp(2 * x1) - 3.75887
     f <- f + 0.2 * x2^11 * (10 * (1 - x2))^6 + 10 * (10 * x2)^3 * (1 - x2)^10 - 1.396
     e <- rnorm(n, 0, sqrt(abs(sig2)))
     y <- f + e
     b<-gam(y~s(x0)+s(x1)+s(x0,x1)+s(x2))
     plot(b,pages=1)
     test1<-function(x,z,sx=0.3,sz=0.4)  
     { (pi**sx*sz)*(1.2*exp(-(x-0.2)^2/sx^2-(z-0.3)^2/sz^2)+
       0.8*exp(-(x-0.7)^2/sx^2-(z-0.8)^2/sz^2))
     }
     n<-500
     old.par<-par(mfrow=c(2,2))
     x<-runif(n);z<-runif(n);
     y<-test1(x,z)+rnorm(n)*0.1
     b<-gam(y~s(x)+s(z)+s(x,z))
     plot(b)
     par(old.par)
     rm(list=c("f","x0","x1","x2","x","z","y","b","test1","n","sig2","pi","e"))

